ISR Projects by James A. Jones

One of the most difficult tasks in debugging software for a developer is to understand the nature of the fault. Techniques have been proposed by researchers that can help *locate* the fault, but mostly neglected is a way to describe the nature of the fault. We are developing software models, visualizations, and techniques to aid in the diagnosis of the faults in the software.

In addition to the dynamic nature of software while executing, this dynamism extends to the evolution of the software's code itself. The software's evolution is often captured in its entirety by revision-control systems (such as CVS, Subversion, and Git). By utilizing this rich artifact, as well as other historical artifacts (e.g., bug-tracking systems and mailing lists), we can offer a number of techniques for recommending future actions to developers.

In order to produce effective fault-localization, debugging, failure-clustering, and test-suite maintenance techniques, researchers would benefit from a deeper understanding of how faults (i.e., bugs) behave and interact with each other. Some faults, even if executed, may or may not propagate to the output, and even still may or may not influence the output in a way to cause failure. Furthermore, in the presence of multiple faults, faults may interact in a way to obscure each other or in a way to produce behavior not seen in their isolation.

One of the many challenges of software development and maintenance is the need to collaborate among many constituents and stakeholders. For example, clients interact with software development organizations; software-development organizations consist of many developers and maintainers within the same location and across different locations; and the development organization often outsources some of the testing efforts to independent test agencies. Each of these parties may reside in different locations, often across many very disparate time zones.

We developed techniques for clustering of failures. Failure-clustering techniques attempt to categorize failing test cases according to the bugs that caused them. Test cases are clustered by utilizing their execution profiles (gathered from instrumented versions of the code) as a means to encode the behavior of those executions. Such techniques can offer suggestions for duplicate submissions of bug reports.

Test suites often need to adapt to the software that it is intended to test. The core software changes and grows, and as such, its test suite also needs to change and grow. However, the test suites can often grow so large as to be unmaintainable. We have developed techniques to assist in the maintenance of these test suites, specifically in allowing for test-suite reduction (while preserving coverage adequacy) and test-suite prioritization.

One method of facilitating developers to understand the complex inner nature of software that we have employed is the use of information visualization. Software is often so complex that even the developers who initially created it cannot understand all of the possible runtime behaviors that it can exhibit --- specifically, all of the bugs that it may contain. In order to present large code bases with innumerable characteristics and relationships of its components (e.g., instructions, variables, values, and timings) we have developed a number of novel visualizations of software.

We developed a fault-localization technique that utilized correlation-based heuristics. The technique and tool was called Tarantula. Tarantula uses the pass/fail statuses of test cases and the events that occurred during execution of each test case to offer the developer recommendations of what may be the faults that are causing test-case failures. The intuition of the approach is to find correlations between execution events and test-case outcomes --- those events that correlate most highly with failure are suggested as places to begin investigation.

To enable much of our research to enable program understanding, software quality, and maintenance, we utilize and develop analyses of program code. These analyses model the flows of information through the logic of programs and systems. With these analysis models enable automated techniques to assist development and maintenance tasks.